You're debating data quality priorities with colleagues. How can you ensure the best approach is chosen?

It's important to understand the business needs of data quality and that goes beyond a single data quality dimension. While accuracy might be more important in some cases, completeness might be more important in other cases. If you're governing your data quality, you've already identified the people who should be involved in the discussion. You need a cross-functional perspective to make the right decision for the company. Voices need to be heard and people need to listen to others.

Basado en la experiencia, una de las formas más comunes de comprender las necesidades para no perder el enfoque es interiorizar la visión, así como el objetivo principal para llegar al cumplimiento de esa visión, entrar en el ejercicio de preguntarse si lo que se está haciendo o se planea hacer responde a las necesidades y aporta valor significativo al objetivo en pro de la visión, entonces se dirige por el camino correcto. Es decir, tomar decisiones teniendo cómo criterio central, la estrategia corporativa.

1. **Define Clear Objectives**: Start by agreeing on the specific goals and objectives of data quality within your organization or project. Knowing what you're aiming for (e.g., accuracy, completeness, consistency) will guide your decision-making. 2. **Use Data Quality Frameworks**: Reference established data quality frameworks like DAMA-DMBOK or ISO 8000. These frameworks provide structured methodologies and best practices that can serve as a foundation for your approach. 3. **Consider Context and Use Case**: Evaluate the specific context in which the data will be used. Different use cases might prioritize different aspects of data quality, such as timeliness in real-time systems or completeness in regulatory reporting.

1 Informed Decision-Making**: Data helps individuals and organizations make informed choices based on evidence rather than guesswork. This leads to more accurate, reliable, and objective decisions. 2. **Trend Identification and Forecasting**: By analyzing historical data, patterns and trends can be identified, helping predict future events, behaviors, or market movements. This is critical for planning, investments, and strategic development. 3. **Improvement of Products and Services**: Companies collect data on customer behavior, feedback, and preferences to improve their offerings. This leads to better customer satisfaction and competitiveness in the market. 4. **Problem Solving**: Data helps identify problems and

In my opinion, the answer goes via data integration. Seeking, finding and negotiang common ground arond data integrity may prove one way to adreess the chalenge. Maintaining data integrity emphasize the importance of data quality, via data profiling and cleansing. The process helps identify and rectify any inconsistencies, duplicates or incomplete records in the dataset. Implementing following process may help: - data profiling and cleansing - backup strategy - data validation checks -> ensuring accuracy - incremental migration -> transfering data in smaller chunks, rather all at onnce - understanding data security while migrating

We mainly talk about the data collection and then setting standards for getting the best data. However, making data collection streamlined and getting access to the roots for quality data is something different. If you want to get the best data, which means good data, then you need better data mansgemenst tools that can collect data from the roots. Indeed, setting standards from the roots is very necessary.

Data management and quality data management are two different things. Indeed, the problems that arise in such a situations are not possible for everyone to deal with, the tools, processes, and most importantly the people (related) who are involved. Now, successful implementation of solutions require a deep understanding of the problem and its longterms aspects. Indeed, the proposed solutions must be checked under a set of quality data to further analyze the future perspective and outcomes! Thus, you can easily manage the problem with a longer-term but scalable solution.

Indeed, monitoring is the last but new phase of dealing with the problems. You require new quality data after the implementation of the proposed solution. Indeed, quality monitoring requires trained people and more goal-oriented standards. In addition, the monitoring process can decide your organization's future as it involves the critical analysis of your processes and working culture. Indeed, quality data management is the tool for any organization in today's AI-driven businesses. You have nothing if you have no quality data management system with accuracy, preciseness, and sharp management practices.